Automated diagnosis of skin cancer is an important area of research that had different automated learning methods proposed so far. However, models based on insufficient labeled training data can badly influence the di...Automated diagnosis of skin cancer is an important area of research that had different automated learning methods proposed so far. However, models based on insufficient labeled training data can badly influence the diagnosis results if there is no advising and semi supervising capability in the model to add unlabeled data in the training set to get sufficient information. This paper proposes a semi-advised support vector machine based classification algorithm that can be trained using labeled data together with abundant unlabeled data. Adaptive differential evolution based algorithm is used for feature selection. For experimental analysis two type of skin cancer datasets are used, one is based on digital dermoscopic images and other is based on histopathological images. The proposed model provided quite convincing results on both the datasets, when compared with respective state-of-the art methods used for feature selection and classification phase.展开更多
文摘Automated diagnosis of skin cancer is an important area of research that had different automated learning methods proposed so far. However, models based on insufficient labeled training data can badly influence the diagnosis results if there is no advising and semi supervising capability in the model to add unlabeled data in the training set to get sufficient information. This paper proposes a semi-advised support vector machine based classification algorithm that can be trained using labeled data together with abundant unlabeled data. Adaptive differential evolution based algorithm is used for feature selection. For experimental analysis two type of skin cancer datasets are used, one is based on digital dermoscopic images and other is based on histopathological images. The proposed model provided quite convincing results on both the datasets, when compared with respective state-of-the art methods used for feature selection and classification phase.
